Reduction and predictive control design for a computational fluid dynamics model

Patricia Astrid; Leo Huisman; Siep Weiland; A. C.P.M. Backx

Reduction and predictive control design for a computational fluid dynamics model

Patricia Astrid
, Leo Huisman
, Siep Weiland
, A. C.P.M. Backx

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Many models of commercial industrial applications are based on computational fluid dynamics (CFD) models. The models are usually of high order that it becomes infeasible to control or to optimize them. In this paper, it is shown that CFD models can be reduced very effectively by applying Proper Orthogonal Decomposition. The resulting reduced CFD model has a state space structure and therefore enables application of many well-known control design, including Model Predictive Controllers.

Original language	English
Title of host publication	41st IEEE Conference on Decision and Control
Pages	3378-3383
Number of pages	6
Volume	3
Publication status	Published - 2002
Event	41st IEEE Conference on Decision and Control (CDC 2002) - Las Vegas, United States Duration: 10 Dec 2002 → 13 Dec 2002 Conference number: 41

Conference

Conference	41st IEEE Conference on Decision and Control (CDC 2002)
Abbreviated title	CDC 2002
Country/Territory	United States
City	Las Vegas
Period	10/12/02 → 13/12/02

Bibliographical note

Copyright:
Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.

Cite this

@inproceedings{d76dee21655249808a2a350b0e47176c,

title = "Reduction and predictive control design for a computational fluid dynamics model",

abstract = "Many models of commercial industrial applications are based on computational fluid dynamics (CFD) models. The models are usually of high order that it becomes infeasible to control or to optimize them. In this paper, it is shown that CFD models can be reduced very effectively by applying Proper Orthogonal Decomposition. The resulting reduced CFD model has a state space structure and therefore enables application of many well-known control design, including Model Predictive Controllers.",

author = "Patricia Astrid and Leo Huisman and Siep Weiland and Backx, \{A. C.P.M.\}",

note = "Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.; 41st IEEE Conference on Decision and Control (CDC 2002), CDC 2002 ; Conference date: 10-12-2002 Through 13-12-2002",

year = "2002",

language = "English",

volume = "3",

pages = "3378--3383",

booktitle = "41st IEEE Conference on Decision and Control",

}

TY - GEN

T1 - Reduction and predictive control design for a computational fluid dynamics model

AU - Astrid, Patricia

AU - Huisman, Leo

AU - Weiland, Siep

AU - Backx, A. C.P.M.

N1 - Conference code: 41

PY - 2002

Y1 - 2002

N2 - Many models of commercial industrial applications are based on computational fluid dynamics (CFD) models. The models are usually of high order that it becomes infeasible to control or to optimize them. In this paper, it is shown that CFD models can be reduced very effectively by applying Proper Orthogonal Decomposition. The resulting reduced CFD model has a state space structure and therefore enables application of many well-known control design, including Model Predictive Controllers.

AB - Many models of commercial industrial applications are based on computational fluid dynamics (CFD) models. The models are usually of high order that it becomes infeasible to control or to optimize them. In this paper, it is shown that CFD models can be reduced very effectively by applying Proper Orthogonal Decomposition. The resulting reduced CFD model has a state space structure and therefore enables application of many well-known control design, including Model Predictive Controllers.

UR - https://www.scopus.com/pages/publications/0242500403

M3 - Conference contribution

AN - SCOPUS:0242500403

VL - 3

SP - 3378

EP - 3383

BT - 41st IEEE Conference on Decision and Control

T2 - 41st IEEE Conference on Decision and Control (CDC 2002)

Y2 - 10 December 2002 through 13 December 2002

ER -

Reduction and predictive control design for a computational fluid dynamics model

Abstract

Conference

Bibliographical note

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